



import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
    import torch

@require_torch
@require_optimum
@slow
class    lowercase		(      unittest.TestCase					):




       '''simple docstring'''







       def 							snake_case_					(		self  )   ->						Optional[int]:

           """simple docstring"""
           UpperCAmelCase      =       '''hf-internal-testing/tiny-random-t5'''
           UpperCAmelCase      =       AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_  )
           UpperCAmelCase      =       AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_  )

           UpperCAmelCase      =       tokenizer('''This is me'''						,			return_tensors='''pt'''  )

           UpperCAmelCase      =       model.to_bettertransformer()

           self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()  )  )

           UpperCAmelCase      =       model.generate(**SCREAMING_SNAKE_CASE_  )

           UpperCAmelCase      =       model.reverse_bettertransformer()

           self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()  )  )

           with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_pretrained(SCREAMING_SNAKE_CASE_  )

               UpperCAmelCase      =       AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_  )

               self.assertFalse(
                   any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()  )  )

               UpperCAmelCase      =       model_reloaded.generate(**SCREAMING_SNAKE_CASE_  )
               self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_  )  )






       def 							snake_case_					(		self  )   ->						Tuple:

           """simple docstring"""
           UpperCAmelCase      =       '''hf-internal-testing/tiny-random-t5'''
           UpperCAmelCase      =       AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE_  )

           UpperCAmelCase      =       model.to_bettertransformer()

           with tempfile.TemporaryDirectory() as tmpdirname:
               with self.assertRaises(SCREAMING_SNAKE_CASE_  ):
                   model.save_pretrained(SCREAMING_SNAKE_CASE_  )

               UpperCAmelCase      =       model.reverse_bettertransformer()
               model.save_pretrained(SCREAMING_SNAKE_CASE_  )






"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase        =				logging.get_logger(__name__)

UpperCAmelCase        =				{"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}



class 			lowercase					( lowercase__		):
			lowercase        =     '''openai-gpt'''
			lowercase        =     {
			    '''max_position_embeddings''': '''n_positions''',
			    '''hidden_size''': '''n_embd''',
			    '''num_attention_heads''': '''n_head''',
			    '''num_hidden_layers''': '''n_layer''',
			}






			def __init__(self  :	List[Any]      ,SCREAMING_SNAKE_CASE_  :	str=40_478      ,SCREAMING_SNAKE_CASE_  :	Optional[int]=512      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]=768      ,SCREAMING_SNAKE_CASE_  :	Dict=12      ,SCREAMING_SNAKE_CASE_  :	Dict=12      ,SCREAMING_SNAKE_CASE_  :	int="gelu"      ,SCREAMING_SNAKE_CASE_  :	Dict=0.1      ,SCREAMING_SNAKE_CASE_  :	List[Any]=0.1      ,SCREAMING_SNAKE_CASE_  :	Dict=0.1      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]=1e-5      ,SCREAMING_SNAKE_CASE_  :	List[str]=0.02      ,SCREAMING_SNAKE_CASE_  :	Union[str, Any]="cls_index"      ,SCREAMING_SNAKE_CASE_  :	str=True      ,SCREAMING_SNAKE_CASE_  :	Any=None      ,SCREAMING_SNAKE_CASE_  :	List[Any]=True      ,SCREAMING_SNAKE_CASE_  :	List[str]=0.1      ,**SCREAMING_SNAKE_CASE_  :	int      ,)       ->	int:





				"""simple docstring"""


				lowerCAmelCase												=  vocab_size
				lowerCAmelCase												=  n_positions
				lowerCAmelCase												=  n_embd
				lowerCAmelCase												=  n_layer
				lowerCAmelCase												=  n_head
				lowerCAmelCase												=  afn
				lowerCAmelCase												=  resid_pdrop
				lowerCAmelCase												=  embd_pdrop
				lowerCAmelCase												=  attn_pdrop
				lowerCAmelCase												=  layer_norm_epsilon
				lowerCAmelCase												=  initializer_range
				lowerCAmelCase												=  summary_type
				lowerCAmelCase												=  summary_use_proj
				lowerCAmelCase												=  summary_activation
				lowerCAmelCase												=  summary_first_dropout
				lowerCAmelCase												=  summary_proj_to_labels
				super().__init__(**SCREAMING_SNAKE_CASE_			)


